Predicting metabolite response to dietary intervention using deep learning
Abstract Due to highly personalized biological and lifestyle characteristics, different individuals may have different metabolite responses to specific foods and nutrients. In particular, the gut microbiota, a collection of trillions of microorganisms living in the gastrointestinal tract, is highly...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56165-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832594580556480512 |
|---|---|
| author | Tong Wang Hannah D. Holscher Sergei Maslov Frank B. Hu Scott T. Weiss Yang-Yu Liu |
| author_facet | Tong Wang Hannah D. Holscher Sergei Maslov Frank B. Hu Scott T. Weiss Yang-Yu Liu |
| author_sort | Tong Wang |
| collection | DOAJ |
| description | Abstract Due to highly personalized biological and lifestyle characteristics, different individuals may have different metabolite responses to specific foods and nutrients. In particular, the gut microbiota, a collection of trillions of microorganisms living in the gastrointestinal tract, is highly personalized and plays a key role in the metabolite responses to foods and nutrients. Accurately predicting metabolite responses to dietary interventions based on individuals’ gut microbial compositions holds great promise for precision nutrition. Existing prediction methods are typically limited to traditional machine learning models. Deep learning methods dedicated to such tasks are still lacking. Here we develop a method McMLP (Metabolite response predictor using coupled Multilayer Perceptrons) to fill in this gap. We provide clear evidence that McMLP outperforms existing methods on both synthetic data generated by the microbial consumer-resource model and real data obtained from six dietary intervention studies. Furthermore, we perform sensitivity analysis of McMLP to infer the tripartite food-microbe-metabolite interactions, which are then validated using the ground-truth (or literature evidence) for synthetic (or real) data, respectively. The presented tool has the potential to inform the design of microbiota-based personalized dietary strategies to achieve precision nutrition. |
| format | Article |
| id | doaj-art-d8a1a07643a742e8ab0656231a014d4c |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-d8a1a07643a742e8ab0656231a014d4c2025-01-19T12:30:49ZengNature PortfolioNature Communications2041-17232025-01-0116111210.1038/s41467-025-56165-6Predicting metabolite response to dietary intervention using deep learningTong Wang0Hannah D. Holscher1Sergei Maslov2Frank B. Hu3Scott T. Weiss4Yang-Yu Liu5Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolDepartment of Food Science and Human Nutrition, University of Illinois at Urbana-ChampaignCenter for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-ChampaignChanning Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolChanning Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolChanning Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical SchoolAbstract Due to highly personalized biological and lifestyle characteristics, different individuals may have different metabolite responses to specific foods and nutrients. In particular, the gut microbiota, a collection of trillions of microorganisms living in the gastrointestinal tract, is highly personalized and plays a key role in the metabolite responses to foods and nutrients. Accurately predicting metabolite responses to dietary interventions based on individuals’ gut microbial compositions holds great promise for precision nutrition. Existing prediction methods are typically limited to traditional machine learning models. Deep learning methods dedicated to such tasks are still lacking. Here we develop a method McMLP (Metabolite response predictor using coupled Multilayer Perceptrons) to fill in this gap. We provide clear evidence that McMLP outperforms existing methods on both synthetic data generated by the microbial consumer-resource model and real data obtained from six dietary intervention studies. Furthermore, we perform sensitivity analysis of McMLP to infer the tripartite food-microbe-metabolite interactions, which are then validated using the ground-truth (or literature evidence) for synthetic (or real) data, respectively. The presented tool has the potential to inform the design of microbiota-based personalized dietary strategies to achieve precision nutrition.https://doi.org/10.1038/s41467-025-56165-6 |
| spellingShingle | Tong Wang Hannah D. Holscher Sergei Maslov Frank B. Hu Scott T. Weiss Yang-Yu Liu Predicting metabolite response to dietary intervention using deep learning Nature Communications |
| title | Predicting metabolite response to dietary intervention using deep learning |
| title_full | Predicting metabolite response to dietary intervention using deep learning |
| title_fullStr | Predicting metabolite response to dietary intervention using deep learning |
| title_full_unstemmed | Predicting metabolite response to dietary intervention using deep learning |
| title_short | Predicting metabolite response to dietary intervention using deep learning |
| title_sort | predicting metabolite response to dietary intervention using deep learning |
| url | https://doi.org/10.1038/s41467-025-56165-6 |
| work_keys_str_mv | AT tongwang predictingmetaboliteresponsetodietaryinterventionusingdeeplearning AT hannahdholscher predictingmetaboliteresponsetodietaryinterventionusingdeeplearning AT sergeimaslov predictingmetaboliteresponsetodietaryinterventionusingdeeplearning AT frankbhu predictingmetaboliteresponsetodietaryinterventionusingdeeplearning AT scotttweiss predictingmetaboliteresponsetodietaryinterventionusingdeeplearning AT yangyuliu predictingmetaboliteresponsetodietaryinterventionusingdeeplearning |